Overview

Dataset statistics

Number of variables24
Number of observations145460
Missing cells3268
Missing cells (%)0.1%
Duplicate rows6
Duplicate rows (%)< 0.1%
Total size in memory26.6 MiB
Average record size in memory192.0 B

Variable types

Categorical4
Numeric18
Boolean2

Alerts

Dataset has 6 (< 0.1%) duplicate rowsDuplicates
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 2 other fieldsHigh correlation
Evaporation is highly correlated with SunshineHigh correlation
Sunshine is highly correlated with EvaporationHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeedHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Humidity9am is highly correlated with Humidity3pmHigh correlation
Humidity3pm is highly correlated with Humidity9am and 1 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Cloud3pmHigh correlation
Cloud3pm is highly correlated with Cloud9amHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 3 other fieldsHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 4 other fieldsHigh correlation
Evaporation is highly correlated with SunshineHigh correlation
Sunshine is highly correlated with EvaporationHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with MaxTemp and 1 other fieldsHigh correlation
Humidity3pm is highly correlated with MaxTemp and 2 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Cloud3pmHigh correlation
Cloud3pm is highly correlated with Cloud9amHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 3 other fieldsHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 2 other fieldsHigh correlation
Evaporation is highly correlated with SunshineHigh correlation
Sunshine is highly correlated with EvaporationHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Cloud3pmHigh correlation
Cloud3pm is highly correlated with Cloud9amHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 2 other fieldsHigh correlation
Location is highly correlated with MinTemp and 14 other fieldsHigh correlation
MinTemp is highly correlated with Location and 4 other fieldsHigh correlation
MaxTemp is highly correlated with Location and 6 other fieldsHigh correlation
Sunshine is highly correlated with Location and 2 other fieldsHigh correlation
WindGustDir is highly correlated with Location and 2 other fieldsHigh correlation
WindGustSpeed is highly correlated with Location and 2 other fieldsHigh correlation
WindDir9am is highly correlated with Location and 2 other fieldsHigh correlation
WindDir3pm is highly correlated with Location and 2 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeedHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Humidity9am is highly correlated with Location and 4 other fieldsHigh correlation
Humidity3pm is highly correlated with Location and 4 other fieldsHigh correlation
Pressure9am is highly correlated with Location and 1 other fieldsHigh correlation
Pressure3pm is highly correlated with Location and 1 other fieldsHigh correlation
Cloud9am is highly correlated with Location and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Location and 2 other fieldsHigh correlation
Temp9am is highly correlated with Location and 5 other fieldsHigh correlation
Temp3pm is highly correlated with Location and 6 other fieldsHigh correlation
RainTomorrow is highly correlated with Humidity3pmHigh correlation
Month is highly correlated with MinTemp and 3 other fieldsHigh correlation
RainTomorrow has 3267 (2.2%) missing values Missing
Rainfall has 94341 (64.9%) zeros Zeros
Evaporation has 46979 (32.3%) zeros Zeros
Sunshine has 55528 (38.2%) zeros Zeros
WindGustSpeed has 6079 (4.2%) zeros Zeros
WindSpeed9am has 8745 (6.0%) zeros Zeros
Pressure9am has 12119 (8.3%) zeros Zeros
Pressure3pm has 12119 (8.3%) zeros Zeros
Cloud9am has 43609 (30.0%) zeros Zeros
Cloud3pm has 39941 (27.5%) zeros Zeros

Reproduction

Analysis started2021-12-18 13:56:55.774620
Analysis finished2021-12-18 13:58:08.261240
Duration1 minute and 12.49 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Location
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3436
Sydney
 
3344
Darwin
 
3193
Melbourne
 
3193
Brisbane
 
3193
Other values (44)
129101 

Length

Max length16
Median length8
Mean length8.711625189
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Canberra3436
 
2.4%
Sydney3344
 
2.3%
Darwin3193
 
2.2%
Melbourne3193
 
2.2%
Brisbane3193
 
2.2%
Adelaide3193
 
2.2%
Perth3193
 
2.2%
Hobart3193
 
2.2%
Albany3040
 
2.1%
MountGambier3040
 
2.1%
Other values (39)113442
78.0%

Length

2021-12-18T17:58:08.369401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra3436
 
2.4%
sydney3344
 
2.3%
darwin3193
 
2.2%
melbourne3193
 
2.2%
brisbane3193
 
2.2%
adelaide3193
 
2.2%
perth3193
 
2.2%
hobart3193
 
2.2%
launceston3040
 
2.1%
wollongong3040
 
2.1%
Other values (39)113442
78.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MinTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct582
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.197524
Minimum-8.5
Maximum33.9
Zeros159
Zeros (%)0.1%
Negative3496
Negative (%)2.4%
Memory size1.1 MiB
2021-12-18T17:58:08.510160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.8
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.385999383
Coefficient of variation (CV)0.5235488272
Kurtosis-0.4769970543
Mean12.197524
Median Absolute Deviation (MAD)4.6
Skewness0.01888407296
Sum1774251.84
Variance40.78098812
MonotonicityNot monotonic
2021-12-18T17:58:08.715620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11899
 
0.6%
10.2898
 
0.6%
9.6896
 
0.6%
10.5884
 
0.6%
10.8872
 
0.6%
9872
 
0.6%
10871
 
0.6%
12866
 
0.6%
8.9861
 
0.6%
10.4860
 
0.6%
Other values (572)136681
94.0%
ValueCountFrequency (%)
-8.51
 
< 0.1%
-8.22
 
< 0.1%
-82
 
< 0.1%
-7.81
 
< 0.1%
-7.62
 
< 0.1%
-7.52
 
< 0.1%
-7.31
 
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-77
< 0.1%
ValueCountFrequency (%)
33.91
 
< 0.1%
31.91
 
< 0.1%
31.81
 
< 0.1%
31.43
< 0.1%
31.21
 
< 0.1%
311
 
< 0.1%
30.72
< 0.1%
30.51
 
< 0.1%
30.31
 
< 0.1%
30.21
 
< 0.1%

MaxTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct690
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.21847574
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Negative113
Negative (%)0.1%
Memory size1.1 MiB
2021-12-18T17:58:08.852690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.9
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.10806124
Coefficient of variation (CV)0.3061381513
Kurtosis-0.2174600328
Mean23.21847574
Median Absolute Deviation (MAD)5.1
Skewness0.2190530828
Sum3377359.482
Variance50.52453459
MonotonicityNot monotonic
2021-12-18T17:58:08.993850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20885
 
0.6%
19843
 
0.6%
19.8840
 
0.6%
20.4834
 
0.6%
19.9823
 
0.6%
20.8817
 
0.6%
19.5812
 
0.6%
18.5811
 
0.6%
21810
 
0.6%
18.2804
 
0.6%
Other values (680)137181
94.3%
ValueCountFrequency (%)
-4.81
< 0.1%
-4.11
< 0.1%
-3.81
< 0.1%
-3.71
< 0.1%
-3.21
< 0.1%
-3.12
< 0.1%
-31
< 0.1%
-2.91
< 0.1%
-2.71
< 0.1%
-2.52
< 0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
47.32
< 0.1%
471
 
< 0.1%
46.91
 
< 0.1%
46.83
< 0.1%
46.72
< 0.1%
46.61
 
< 0.1%
46.51
 
< 0.1%
46.44
< 0.1%
46.32
< 0.1%

Rainfall
Real number (ℝ≥0)

ZEROS

Distinct681
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.307989825
Minimum0
Maximum371
Zeros94341
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:09.230675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.6
95-th percentile12.8
Maximum371
Range371
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation8.389770949
Coefficient of variation (CV)3.635098759
Kurtosis181.9143571
Mean2.307989825
Median Absolute Deviation (MAD)0
Skewness9.940909034
Sum335720.2
Variance70.38825658
MonotonicityNot monotonic
2021-12-18T17:58:09.363437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
094341
64.9%
0.28761
 
6.0%
0.43782
 
2.6%
0.62592
 
1.8%
0.82056
 
1.4%
11759
 
1.2%
1.21535
 
1.1%
1.41377
 
0.9%
1.61200
 
0.8%
1.81104
 
0.8%
Other values (671)26953
 
18.5%
ValueCountFrequency (%)
094341
64.9%
0.1157
 
0.1%
0.28761
 
6.0%
0.365
 
< 0.1%
0.43782
 
2.6%
0.539
 
< 0.1%
0.62592
 
1.8%
0.713
 
< 0.1%
0.82056
 
1.4%
0.915
 
< 0.1%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%
2401
< 0.1%
236.81
< 0.1%
2251
< 0.1%
219.61
< 0.1%
216.31
< 0.1%

Evaporation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct653
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.682469675
Minimum0
Maximum145
Zeros46979
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:09.516197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.8
Q36
95-th percentile10.86038647
Maximum145
Range145
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.177571766
Coefficient of variation (CV)1.134448383
Kurtosis29.5047812
Mean3.682469675
Median Absolute Deviation (MAD)2.8
Skewness2.832390787
Sum535652.0389
Variance17.45210586
MonotonicityNot monotonic
2021-12-18T17:58:09.787293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046979
32.3%
43339
 
2.3%
82609
 
1.8%
2.22095
 
1.4%
22032
 
1.4%
2.62003
 
1.4%
2.42003
 
1.4%
1.81979
 
1.4%
31973
 
1.4%
3.41967
 
1.4%
Other values (643)78481
54.0%
ValueCountFrequency (%)
046979
32.3%
0.18
 
< 0.1%
0.2503
 
0.3%
0.310
 
< 0.1%
0.4769
 
0.5%
0.514
 
< 0.1%
0.61097
 
0.8%
0.724
 
< 0.1%
0.81384
 
1.0%
0.928
 
< 0.1%
ValueCountFrequency (%)
1451
< 0.1%
86.21
< 0.1%
82.41
< 0.1%
81.21
< 0.1%
77.31
< 0.1%
74.81
< 0.1%
72.21
< 0.1%
70.41
< 0.1%
701
< 0.1%
68.82
< 0.1%

Sunshine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct395
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.839923976
Minimum0
Maximum14.5
Zeros55528
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:09.981208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.8
Q39.140909091
95-th percentile12.1
Maximum14.5
Range14.5
Interquartile range (IQR)9.140909091

Descriptive statistics

Standard deviation4.610834461
Coefficient of variation (CV)0.952666712
Kurtosis-1.504265488
Mean4.839923976
Median Absolute Deviation (MAD)4.8
Skewness0.2339436017
Sum704015.3416
Variance21.25979443
MonotonicityNot monotonic
2021-12-18T17:58:10.235594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
055528
38.2%
10.71101
 
0.8%
111094
 
0.8%
10.81069
 
0.7%
10.51027
 
0.7%
10.91021
 
0.7%
10.31010
 
0.7%
10.2993
 
0.7%
10984
 
0.7%
11.1978
 
0.7%
Other values (385)80655
55.4%
ValueCountFrequency (%)
055528
38.2%
0.1542
 
0.4%
0.2521
 
0.4%
0.3433
 
0.3%
0.4326
 
0.2%
0.5322
 
0.2%
0.6298
 
0.2%
0.7344
 
0.2%
0.8320
 
0.2%
0.9323
 
0.2%
ValueCountFrequency (%)
14.51
 
< 0.1%
14.34
 
< 0.1%
14.22
 
< 0.1%
14.16
 
< 0.1%
1415
 
< 0.1%
13.922
 
< 0.1%
13.860
 
< 0.1%
13.7118
0.1%
13.6181
0.1%
13.5183
0.1%

WindGustDir
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.1 MiB
SSE
13367 
SSW
11952 
W
10112 
SE
9602 
SW
9486 
Other values (11)
90940 

Length

Max length3
Median length2
Mean length2.235385916
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowNE
5th rowW

Common Values

ValueCountFrequency (%)
SSE13367
 
9.2%
SSW11952
 
8.2%
W10112
 
7.0%
SE9602
 
6.6%
SW9486
 
6.5%
N9422
 
6.5%
E9391
 
6.5%
S9302
 
6.4%
WSW9231
 
6.3%
WNW8455
 
5.8%
Other values (6)45139
31.0%

Length

2021-12-18T17:58:10.366378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sse13367
 
9.2%
ssw11952
 
8.2%
w10112
 
7.0%
se9602
 
6.6%
sw9486
 
6.5%
n9422
 
6.5%
e9391
 
6.5%
s9302
 
6.4%
wsw9231
 
6.3%
wnw8455
 
5.8%
Other values (6)45139
31.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindGustSpeed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct531
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.3943728
Minimum0
Maximum135
Zeros6079
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:10.543460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q130
median37
Q346
95-th percentile65
Maximum135
Range135
Interquartile range (IQR)16

Descriptive statistics

Standard deviation15.3947337
Coefficient of variation (CV)0.4009632814
Kurtosis1.413489566
Mean38.3943728
Median Absolute Deviation (MAD)9
Skewness0.1476515087
Sum5584845.467
Variance236.9978258
MonotonicityNot monotonic
2021-12-18T17:58:10.867704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359215
 
6.3%
398794
 
6.0%
318428
 
5.8%
378047
 
5.5%
337933
 
5.5%
417369
 
5.1%
307038
 
4.8%
436609
 
4.5%
286478
 
4.5%
06079
 
4.2%
Other values (521)69470
47.8%
ValueCountFrequency (%)
06079
4.2%
61
 
< 0.1%
719
 
< 0.1%
991
 
0.1%
11192
 
0.1%
13532
 
0.4%
15835
 
0.6%
171387
 
1.0%
191751
 
1.2%
202627
1.8%
ValueCountFrequency (%)
1353
 
< 0.1%
1301
 
< 0.1%
1262
 
< 0.1%
1242
 
< 0.1%
1223
 
< 0.1%
1204
< 0.1%
1174
< 0.1%
1155
< 0.1%
1138
< 0.1%
1113
 
< 0.1%

WindDir9am
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
N
12782 
SE
10022 
NW
9854 
SSE
9824 
E
9720 
Other values (11)
93258 

Length

Max length3
Median length2
Mean length2.1833425
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowSE
5th rowENE

Common Values

ValueCountFrequency (%)
N12782
 
8.8%
SE10022
 
6.9%
NW9854
 
6.8%
SSE9824
 
6.8%
E9720
 
6.7%
S9263
 
6.4%
SW9105
 
6.3%
W8865
 
6.1%
NNE8826
 
6.1%
NNW8633
 
5.9%
Other values (6)48566
33.4%

Length

2021-12-18T17:58:11.079518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n12782
 
8.8%
se10022
 
6.9%
nw9854
 
6.8%
sse9824
 
6.8%
e9720
 
6.7%
s9263
 
6.4%
sw9105
 
6.3%
w8865
 
6.1%
nne8826
 
6.1%
nnw8633
 
5.9%
Other values (6)48566
33.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindDir3pm
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
SE
11186 
S
10344 
W
10327 
SSE
10167 
WSW
9616 
Other values (11)
93820 

Length

Max length3
Median length2
Mean length2.207589715
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowE
5th rowNW

Common Values

ValueCountFrequency (%)
SE11186
 
7.7%
S10344
 
7.1%
W10327
 
7.1%
SSE10167
 
7.0%
WSW9616
 
6.6%
SW9463
 
6.5%
NE9115
 
6.3%
N9039
 
6.2%
WNW9027
 
6.2%
NW8916
 
6.1%
Other values (6)48260
33.2%

Length

2021-12-18T17:58:11.222386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se11186
 
7.7%
s10344
 
7.1%
w10327
 
7.1%
sse10167
 
7.0%
wsw9616
 
6.6%
sw9463
 
6.5%
ne9115
 
6.3%
n9039
 
6.2%
wnw9027
 
6.2%
nw8916
 
6.1%
Other values (6)48260
33.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindSpeed9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct294
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.02870232
Minimum0
Maximum130
Zeros8745
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:11.388841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.876369629
Coefficient of variation (CV)0.6327292023
Kurtosis1.257151095
Mean14.02870232
Median Absolute Deviation (MAD)6
Skewness0.7815160833
Sum2040615.04
Variance78.7899378
MonotonicityNot monotonic
2021-12-18T17:58:11.544801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
913649
 
9.4%
1313132
 
9.0%
1111728
 
8.1%
1710789
 
7.4%
710783
 
7.4%
1510625
 
7.3%
69118
 
6.3%
198763
 
6.0%
08745
 
6.0%
208063
 
5.5%
Other values (284)40065
27.5%
ValueCountFrequency (%)
08745
6.0%
24609
3.2%
46360
4.4%
4.22362869242
 
< 0.1%
4.38144329946
 
< 0.1%
4.73891625623
 
< 0.1%
4.7617328522
 
< 0.1%
4.8669064751
 
< 0.1%
5.47081712122
 
< 0.1%
5.51
 
< 0.1%
ValueCountFrequency (%)
1301
 
< 0.1%
872
 
< 0.1%
831
 
< 0.1%
744
 
< 0.1%
721
 
< 0.1%
692
 
< 0.1%
674
 
< 0.1%
658
< 0.1%
639
< 0.1%
6111
< 0.1%

WindSpeed3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct295
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.61807112
Minimum0
Maximum87
Zeros1112
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:11.686012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile33
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.755289705
Coefficient of variation (CV)0.4702576142
Kurtosis0.7985550241
Mean18.61807112
Median Absolute Deviation (MAD)6
Skewness0.63601747
Sum2708184.625
Variance76.65509782
MonotonicityNot monotonic
2021-12-18T17:58:11.833475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1312580
 
8.6%
1712539
 
8.6%
2011713
 
8.1%
1511483
 
7.9%
1911263
 
7.7%
1110015
 
6.9%
99753
 
6.7%
249052
 
6.2%
228598
 
5.9%
286553
 
4.5%
Other values (285)41911
28.8%
ValueCountFrequency (%)
01112
 
0.8%
21034
 
0.7%
42249
 
1.5%
63805
2.6%
6.44387755168
 
< 0.1%
75903
4.1%
7.06282722588
 
0.1%
7.81208053791
 
0.1%
8.2916666671
 
< 0.1%
8.34170854349
 
< 0.1%
ValueCountFrequency (%)
871
 
< 0.1%
832
 
< 0.1%
781
 
< 0.1%
762
 
< 0.1%
741
 
< 0.1%
722
 
< 0.1%
693
 
< 0.1%
671
 
< 0.1%
6518
< 0.1%
6313
< 0.1%

Humidity9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct383
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.96410779
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:11.996113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.9287766
Coefficient of variation (CV)0.2744728701
Kurtosis-0.0120650268
Mean68.96410779
Median Absolute Deviation (MAD)13
Skewness-0.4942214748
Sum10031519.12
Variance358.2985835
MonotonicityNot monotonic
2021-12-18T17:58:12.142052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993391
 
2.3%
703026
 
2.1%
693023
 
2.1%
653014
 
2.1%
683011
 
2.1%
712976
 
2.0%
662973
 
2.0%
672950
 
2.0%
742917
 
2.0%
722914
 
2.0%
Other values (373)115265
79.2%
ValueCountFrequency (%)
01
 
< 0.1%
15
 
< 0.1%
28
 
< 0.1%
310
 
< 0.1%
420
 
< 0.1%
527
 
< 0.1%
637
< 0.1%
743
< 0.1%
856
< 0.1%
971
< 0.1%
ValueCountFrequency (%)
1002863
2.0%
993391
2.3%
982099
1.4%
971789
1.2%
96.343511453
 
< 0.1%
961609
1.1%
951639
1.1%
94.6255319130
 
< 0.1%
94.3271889431
 
< 0.1%
941764
1.2%

Humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct385
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.6580643
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:12.328029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile87
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.62910087
Coefficient of variation (CV)0.399339409
Kurtosis-0.4915102985
Mean51.6580643
Median Absolute Deviation (MAD)14
Skewness0.01793314181
Sum7514182.034
Variance425.5598026
MonotonicityNot monotonic
2021-12-18T17:58:12.659278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
592755
 
1.9%
522751
 
1.9%
552738
 
1.9%
572728
 
1.9%
532697
 
1.9%
582643
 
1.8%
542642
 
1.8%
502624
 
1.8%
512621
 
1.8%
602615
 
1.8%
Other values (375)118646
81.6%
ValueCountFrequency (%)
04
 
< 0.1%
126
 
< 0.1%
235
 
< 0.1%
363
 
< 0.1%
4113
 
0.1%
5157
 
0.1%
6242
0.2%
7303
0.2%
8422
0.3%
9481
0.3%
ValueCountFrequency (%)
100400
0.3%
99434
0.3%
98603
0.4%
97403
0.3%
96462
0.3%
95465
0.3%
94559
0.4%
93607
0.4%
92648
0.4%
91617
0.4%

Pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct766
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean932.8511547
Minimum0
Maximum1041
Zeros12119
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:12.822572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11011.4
median1016.8
Q31021.8
95-th percentile1029
Maximum1041
Range1041
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation281.3132276
Coefficient of variation (CV)0.301562823
Kurtosis7.082532695
Mean932.8511547
Median Absolute Deviation (MAD)5.1
Skewness-3.012462791
Sum135692529
Variance79137.13204
MonotonicityNot monotonic
2021-12-18T17:58:12.968415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012119
 
8.3%
1018.4824
 
0.6%
1016.4816
 
0.6%
1017.9789
 
0.5%
1018.7775
 
0.5%
1016.3775
 
0.5%
1017.3769
 
0.5%
1018769
 
0.5%
1015.9768
 
0.5%
1017.8766
 
0.5%
Other values (756)126290
86.8%
ValueCountFrequency (%)
012119
8.3%
980.51
 
< 0.1%
9821
 
< 0.1%
982.21
 
< 0.1%
982.31
 
< 0.1%
982.92
 
< 0.1%
983.71
 
< 0.1%
983.91
 
< 0.1%
984.41
 
< 0.1%
984.62
 
< 0.1%
ValueCountFrequency (%)
10411
 
< 0.1%
1040.91
 
< 0.1%
1040.62
< 0.1%
1040.51
 
< 0.1%
1040.43
< 0.1%
1040.33
< 0.1%
1040.22
< 0.1%
1040.13
< 0.1%
10401
 
< 0.1%
1039.93
< 0.1%

Pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct768
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean930.6620068
Minimum0
Maximum1039.6
Zeros12119
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:13.144173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11009
median1014.4
Q31019.4
95-th percentile1026.6
Maximum1039.6
Range1039.6
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation280.651877
Coefficient of variation (CV)0.3015615497
Kurtosis7.082699164
Mean930.6620068
Median Absolute Deviation (MAD)5.2
Skewness-3.012507883
Sum135374095.5
Variance78765.47606
MonotonicityNot monotonic
2021-12-18T17:58:13.308582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012119
 
8.3%
1015.3786
 
0.5%
1015.5783
 
0.5%
1015.6776
 
0.5%
1015.7773
 
0.5%
1013.5767
 
0.5%
1015.1766
 
0.5%
1015.8765
 
0.5%
1015.4756
 
0.5%
1016747
 
0.5%
Other values (758)126422
86.9%
ValueCountFrequency (%)
012119
8.3%
977.11
 
< 0.1%
978.21
 
< 0.1%
9791
 
< 0.1%
980.22
 
< 0.1%
981.21
 
< 0.1%
981.41
 
< 0.1%
981.91
 
< 0.1%
982.21
 
< 0.1%
982.61
 
< 0.1%
ValueCountFrequency (%)
1039.61
 
< 0.1%
1038.91
 
< 0.1%
1038.51
 
< 0.1%
1038.41
 
< 0.1%
1038.21
 
< 0.1%
10381
 
< 0.1%
1037.92
< 0.1%
1037.82
< 0.1%
1037.73
< 0.1%
1037.61
 
< 0.1%

Cloud9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct376
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.484322095
Minimum0
Maximum9
Zeros43609
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:13.474419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36.517857143
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6.517857143

Descriptive statistics

Standard deviation3.037416689
Coefficient of variation (CV)0.871738205
Kurtosis-1.613127793
Mean3.484322095
Median Absolute Deviation (MAD)3
Skewness0.1187009721
Sum506829.4919
Variance9.22590014
MonotonicityNot monotonic
2021-12-18T17:58:13.628599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
043609
30.0%
719972
13.7%
115687
 
10.8%
814697
 
10.1%
68171
 
5.6%
26500
 
4.5%
35914
 
4.1%
55591
 
3.8%
44555
 
3.1%
6.568627451228
 
0.2%
Other values (366)20536
14.1%
ValueCountFrequency (%)
043609
30.0%
0.65853658541
 
< 0.1%
0.88617886181
 
< 0.1%
115687
 
10.8%
1.3076923082
 
< 0.1%
1.377049182
 
< 0.1%
1.55855855926
 
< 0.1%
1.6107382556
 
< 0.1%
1.6470588241
 
< 0.1%
1.8552036258
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
814697
10.1%
7.06666666768
 
< 0.1%
719972
13.7%
6.87790697793
 
0.1%
6.863636364199
 
0.1%
6.67605633884
 
0.1%
6.62962963117
 
0.1%
6.61073825599
 
0.1%
6.610169492220
 
0.2%

Cloud3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct375
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.485523379
Minimum0
Maximum9
Zeros39941
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:13.778543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.891402715
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.899274516
Coefficient of variation (CV)0.8318046388
Kurtosis-1.512925148
Mean3.485523379
Median Absolute Deviation (MAD)2.891402715
Skewness0.09035768589
Sum507004.2308
Variance8.405792717
MonotonicityNot monotonic
2021-12-18T17:58:13.920712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039941
27.5%
718229
12.5%
114976
 
10.3%
812660
 
8.7%
69105
 
6.3%
27226
 
5.0%
36921
 
4.8%
56815
 
4.7%
45322
 
3.7%
5.5290
 
0.2%
Other values (365)23975
16.5%
ValueCountFrequency (%)
039941
27.5%
0.573770491863
 
< 0.1%
114976
 
10.3%
1.234
 
< 0.1%
1.48636363628
 
< 0.1%
1.94736842163
 
< 0.1%
1.95238095261
 
< 0.1%
1.97156398168
 
< 0.1%
27226
 
5.0%
2.17061611429
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
812660
8.7%
718229
12.5%
6.78378378443
 
< 0.1%
6.78089887662
 
< 0.1%
6.76995305235
 
< 0.1%
6.68544600935
 
< 0.1%
6.59911894321
 
< 0.1%
6.48026315896
 
0.1%
6.475177305124
 
0.1%

Temp9am
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct665
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.95930913
Minimum-7.2
Maximum40.2
Zeros36
Zeros (%)< 0.1%
Negative483
Negative (%)0.3%
Memory size1.1 MiB
2021-12-18T17:58:14.094633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.5
95-th percentile28.1
Maximum40.2
Range47.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.493692663
Coefficient of variation (CV)0.3828984195
Kurtosis-0.328902681
Mean16.95930913
Median Absolute Deviation (MAD)4.6
Skewness0.08274041082
Sum2466901.107
Variance42.1680444
MonotonicityNot monotonic
2021-12-18T17:58:14.245709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17912
 
0.6%
13.8900
 
0.6%
14.8894
 
0.6%
16882
 
0.6%
14876
 
0.6%
15867
 
0.6%
16.6867
 
0.6%
16.5856
 
0.6%
13848
 
0.6%
15.1846
 
0.6%
Other values (655)136712
94.0%
ValueCountFrequency (%)
-7.21
 
< 0.1%
-71
 
< 0.1%
-6.21
 
< 0.1%
-5.91
 
< 0.1%
-5.62
 
< 0.1%
-5.52
 
< 0.1%
-5.32
 
< 0.1%
-5.25
< 0.1%
-4.91
 
< 0.1%
-4.82
 
< 0.1%
ValueCountFrequency (%)
40.21
< 0.1%
39.41
< 0.1%
39.11
< 0.1%
391
< 0.1%
38.91
< 0.1%
38.61
< 0.1%
38.31
< 0.1%
38.21
< 0.1%
381
< 0.1%
37.91
< 0.1%

Temp3pm
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct719
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.70650748
Minimum-5.4
Maximum46.7
Zeros17
Zeros (%)< 0.1%
Negative180
Negative (%)0.1%
Memory size1.1 MiB
2021-12-18T17:58:14.422363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.5
Q116.6
median21.1
Q326.4
95-th percentile33.8
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.954928935
Coefficient of variation (CV)0.3204075525
Kurtosis-0.1286105583
Mean21.70650748
Median Absolute Deviation (MAD)4.8
Skewness0.2289458072
Sum3157428.578
Variance48.37103649
MonotonicityNot monotonic
2021-12-18T17:58:14.678512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20882
 
0.6%
18.5869
 
0.6%
19869
 
0.6%
18.4868
 
0.6%
17.8859
 
0.6%
19.4840
 
0.6%
19.2839
 
0.6%
18839
 
0.6%
17834
 
0.6%
19.3833
 
0.6%
Other values (709)136928
94.1%
ValueCountFrequency (%)
-5.41
 
< 0.1%
-5.11
 
< 0.1%
-4.41
 
< 0.1%
-4.21
 
< 0.1%
-4.11
 
< 0.1%
-41
 
< 0.1%
-3.92
< 0.1%
-3.81
 
< 0.1%
-3.73
< 0.1%
-3.53
< 0.1%
ValueCountFrequency (%)
46.71
 
< 0.1%
46.21
 
< 0.1%
46.13
< 0.1%
45.91
 
< 0.1%
45.82
< 0.1%
45.41
 
< 0.1%
45.32
< 0.1%
45.22
< 0.1%
451
 
< 0.1%
44.91
 
< 0.1%

RainToday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.2 KiB
False
111713 
True
33747 
ValueCountFrequency (%)
False111713
76.8%
True33747
 
23.2%
2021-12-18T17:58:14.791947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

RainTomorrow
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3267
Missing (%)2.2%
Memory size284.2 KiB
False
110316 
True
31877 
(Missing)
 
3267
ValueCountFrequency (%)
False110316
75.8%
True31877
 
21.9%
(Missing)3267
 
2.2%
2021-12-18T17:58:14.834638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Year
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.769751
Minimum2007
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:15.077848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2009
Q12011
median2013
Q32015
95-th percentile2017
Maximum2017
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.537683738
Coefficient of variation (CV)0.00126079187
Kurtosis-1.180677648
Mean2012.769751
Median Absolute Deviation (MAD)2
Skewness-0.04935666893
Sum292777488
Variance6.439838752
MonotonicityNot monotonic
2021-12-18T17:58:15.201131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
201617934
12.3%
201417885
12.3%
201517885
12.3%
200916789
11.5%
201016782
11.5%
201316415
11.3%
201215409
10.6%
201115407
10.6%
20178623
5.9%
20082270
 
1.6%
ValueCountFrequency (%)
200761
 
< 0.1%
20082270
 
1.6%
200916789
11.5%
201016782
11.5%
201115407
10.6%
201215409
10.6%
201316415
11.3%
201417885
12.3%
201517885
12.3%
201617934
12.3%
ValueCountFrequency (%)
20178623
5.9%
201617934
12.3%
201517885
12.3%
201417885
12.3%
201316415
11.3%
201215409
10.6%
201115407
10.6%
201016782
11.5%
200916789
11.5%
20082270
 
1.6%

Month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.399615014
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T17:58:15.315969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.427261608
Coefficient of variation (CV)0.5355418412
Kurtosis-1.191879728
Mean6.399615014
Median Absolute Deviation (MAD)3
Skewness0.03034286711
Sum930888
Variance11.74612213
MonotonicityNot monotonic
2021-12-18T17:58:15.414185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
313361
9.2%
513353
9.2%
113236
9.1%
612684
8.7%
812028
8.3%
1012028
8.3%
712025
8.3%
1111669
8.0%
911640
8.0%
411550
7.9%
Other values (2)21886
15.0%
ValueCountFrequency (%)
113236
9.1%
210793
7.4%
313361
9.2%
411550
7.9%
513353
9.2%
612684
8.7%
712025
8.3%
812028
8.3%
911640
8.0%
1012028
8.3%
ValueCountFrequency (%)
1211093
7.6%
1111669
8.0%
1012028
8.3%
911640
8.0%
812028
8.3%
712025
8.3%
612684
8.7%
513353
9.2%
411550
7.9%
313361
9.2%

Interactions

2021-12-18T17:58:02.458597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:15.516650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:18.928291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:21.805617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:24.688180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:27.521187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:30.499028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:33.489856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:36.459025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:39.365335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.049240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:44.878094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:47.480603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:49.956893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:52.576235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.004169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:57.375357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:59.845905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:02.592118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:15.942316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.052036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:21.933909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:24.849124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:27.646709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:30.638011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:33.616021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:36.597887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:39.503662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.185557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:44.995546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:47.630085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:50.123939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:52.699245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.139485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:57.500209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:59.972871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:02.724040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:16.067299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.181412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:22.110870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:25.161043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:27.907511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:30.776052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:33.753585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:36.851824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:39.739819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.311883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:45.125362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:47.866258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:50.265514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:52.821633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.266604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:57.763183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:00.105114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:02.868084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:16.202431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.325486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:22.288983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:25.366461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:28.036009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:30.911243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:33.905033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:37.035619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:39.870909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.446582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:45.252273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:48.035569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:50.430612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:52.951715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.401108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:57.898174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:00.242895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:03.036892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:16.340450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.458700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:22.478552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:25.546778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:28.342745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:31.097909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:34.041579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:37.180497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:40.002219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.599443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:45.558114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:48.190380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:50.574170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:53.090326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.536907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:58.032652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:00.375885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:03.180066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:16.504114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.589107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:22.654467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:25.746979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:28.538845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:31.258073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:34.187894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:37.316085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:40.165554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.762237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:45.685389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:48.327664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:50.849970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:53.218996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.674144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:58.176121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:00.507765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:03.418305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:16.672877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.730133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:22.806827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:25.886239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:28.771418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:31.412712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:34.333112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:37.553851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:40.387698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:42.892432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:45.811877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:48.453138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:50.982114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:53.356806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.810033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:58.312268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:00.652567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:03.672506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:16.910955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:19.897201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:22.942948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:26.022389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:28.933511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:31.740991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:34.484523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:37.686460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:40.541909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:43.116618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:45.935395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:48.577699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:51.104667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:53.486597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:55.943239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:58.443665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:00.791472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:03.856946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:17.034063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:57:56.069947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:58:00.917815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:58:04.027310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:17.258556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:20.295145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:23.219397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:26.297497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:29.256858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:57:37.974829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:57:43.515473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:46.185975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:48.822926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:51.401899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:53.757173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T17:57:56.196292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:57:48.961959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:57:56.322744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-18T17:58:02.310465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2021-12-18T17:58:15.562472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-18T17:58:15.811387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-18T17:58:16.087334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-18T17:58:16.414106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-18T17:58:16.582081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-18T17:58:06.114786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-18T17:58:06.811549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-18T17:58:07.653316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-18T17:58:07.874656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowYearMonth
0Albury13.422.90.60.00.0W44.0WWNW20.024.071.022.01007.71007.18.0000004.90109916.921.8NoNo200812
1Albury7.425.10.00.00.0WNW44.0NNWWSW4.022.044.025.01010.61007.85.5396834.90109917.224.3NoNo200812
2Albury12.925.70.00.00.0WSW46.0WWSW19.026.038.030.01007.61008.75.5396832.00000021.023.2NoNo200812
3Albury9.228.00.00.00.0NE24.0SEE11.09.045.016.01017.61012.85.5396834.90109918.126.5NoNo200812
4Albury17.532.31.00.00.0W41.0ENENW7.020.082.033.01010.81006.07.0000008.00000017.829.7NoNo200812
5Albury14.629.70.20.00.0WNW56.0WW19.024.055.023.01009.21005.45.5396834.90109920.628.9NoNo200812
6Albury14.325.00.00.00.0W50.0SWW20.024.049.019.01009.61008.21.0000004.90109918.124.6NoNo200812
7Albury7.726.70.00.00.0W35.0SSEW6.017.048.019.01013.41010.15.5396834.90109916.325.5NoNo200812
8Albury9.731.90.00.00.0NNW80.0SENW7.028.042.09.01008.91003.65.5396834.90109918.330.2NoYes200812
9Albury13.130.11.40.00.0W28.0SSSE15.011.058.027.01007.01005.75.5396834.90109920.128.2YesNo200812

Last rows

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowYearMonth
145450Uluru5.224.3000000.00.00.0E24.000000SEE11.011.053.024.01023.81020.05.7142864.512.323.3NoNo20176
145451Uluru6.423.4000000.00.00.0ESE31.000000SESE15.017.053.025.01025.81023.05.7142864.511.223.1NoNo20176
145452Uluru8.020.7000000.00.00.0ESE41.000000SEE19.026.056.032.01028.11024.35.7142867.011.620.0NoNo20176
145453Uluru7.420.6000000.00.00.0E35.000000ESEE15.017.063.033.01027.21023.35.7142864.511.020.3NoNo20176
145454Uluru3.521.8000000.00.00.0E31.000000ESEE15.013.059.027.01024.71021.25.7142864.59.420.9NoNo20176
145455Uluru2.823.4000000.00.00.0E31.000000SEENE13.011.051.024.01024.61020.35.7142864.510.122.4NoNo20176
145456Uluru3.625.3000000.00.00.0NNW22.000000SEN13.09.056.021.01023.51019.15.7142864.510.924.5NoNo20176
145457Uluru5.426.9000000.00.00.0N37.000000SEWNW9.09.053.024.01021.01016.85.7142864.512.526.1NoNo20176
145458Uluru7.827.0000000.00.00.0SE28.000000SSEN13.07.051.024.01019.41016.53.0000002.015.126.0NoNo20176
145459Uluru14.920.2256940.00.00.0NaN33.923077ESEESE17.017.062.036.01020.21017.98.0000008.015.020.9NoNaN20176

Duplicate rows

Most frequently occurring

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowYearMonth# duplicates
3Newcastle13.09579425.0227270.00.00.0SSW0.0NENE6.63636412.56410367.22171951.2292990.00.03.8409093.87898118.65610922.994904NoNo20101012
4Newcastle17.79220827.2333330.00.00.0SSW0.0NNE4.22362910.31351479.65145259.5752690.00.04.4380174.44148921.16597525.883511NoNo201637
1Newcastle10.72058821.3182170.00.00.0SSW0.0NWNW5.4708177.06282777.70817159.3854170.00.03.9803924.58333314.94824919.444271NoNo201055
2Newcastle10.75514022.7722730.00.00.0SSW0.0SWNW7.60000012.57961865.02272747.6687900.00.03.1538463.30573216.98461521.657325NoNo200993
5Newcastle19.49652529.5647730.00.00.0SSW0.0NENE6.98841713.51562572.45627457.8061220.00.04.5399244.29230823.80114127.652551NoNo201413
0Newcastle9.22314418.3179690.00.00.0SSW0.0NWNE5.6102366.44387880.59375063.6391750.00.04.2500004.60714312.52656316.636735NoNo201662